A First Course in Statistics / Edition 11

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Overview

Classic, yet contemporary. Theoretical, yet applied. McClave & Sincich’s Statistics gives you the best of both worlds. This text offers a trusted, comprehensive introduction to statistics that emphasizes inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data.

The Twelfth Edition infuses a new focus on ethics, which is critically important when working with statistical data. Chapter Summaries have a new, study-oriented design, helping students stay focused when preparing for exams. Data, exercises, technology support, and Statistics in Action cases are updated throughout the book.

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Product Details

  • ISBN-13: 9780321755957
  • Publisher: Pearson
  • Publication date: 1/10/2012
  • Edition number: 11
  • Pages: 624
  • Sales rank: 1,116,089
  • Product dimensions: 8.40 (w) x 10.80 (h) x 1.00 (d)

Meet the Author

Dr. Jim McClave is currently President and CEO of Info Tech, Inc., a statistical consulting and software development firm with an international clientele. He is also currently an Adjunct Professor of Statistics at the University of Florida, where he was a full-time member of the faculty for twenty years.

Dr. Terry Sincich obtained his PhD in Statistics from the University of Florida in 1980. He is an Associate Professor in the Information Systems & Decision Sciences Department at the University of South Florida in Tampa. Dr. Sincich is responsible for teaching basic statistics to all undergraduates, as well as advanced statistics to all doctoral candidates, in the College of Business Administration. He has published articles in such journals as the Journal of the American Statistical Association, International Journal of Forecasting, Academy of Management Journal, and the Auditing: A Journal of Practice & Theory. Dr. Sincich is a co-author of the texts Statistics, Statistics for Business & Economics, Statistics for Engineering & the Sciences, and A Second Course in Statistics: Regression Analysis.

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Table of Contents

1. Statistics, Data, and Statistical Thinking

1.1 The Science of Statistics

1.2 Types of Statistical Applications

1.3 Fundamental Elements of Statistics

1.4 Types of Data

1.5 Collecting Data

1.6 The Role of Statistics in Critical Thinking

2. Methods for Describing Sets of Data

2.1 Describing Qualitative Data

2.2 Graphical Methods for Describing Quantitative Data

2.3 Summation Notation

2.4 Numerical Measures of Central Tendency

2.5 Numerical Measures of Variability

2.6 Interpreting the Standard Deviation

2.7 Numerical Measures of Relative Standing

2.8 Methods for Detecting Outliers: Box Plots and z-Scores

2.9 Graphing Bivariate Relationships (Optional)

2.10 Distorting the Truth with Descriptive Techniques

3. Probability

3.1 Events, Sample Spaces, and Probability

3.2 Unions and Intersections

3.3 Complementary Events

3.4 The Additive Rule and Mutually Exclusive Events

3.5 Conditional Probability

3.6 The Multiplicative Rule and Independent Events

3.7 Random Sampling

3.8 Some Additional Counting Rules (Optional)

3.9 Bayes’ Rule (Optional)

4. Random Variables and Probability Distributions

4.1 Two Types of Random Variables

4.2 Probability Distributions for Discrete Random Variables

4.3 Expected Values of Discrete Random Variables

4.4 The Binomial Random Variable

4.5 Continuous Probability Distributions

4.6 The Normal Distribution

4.7 Descriptive Methods for Assessing Normality

4.8 Approximating a Binomial Distribution with a Normal Distribution (Optional)

4.9 What is a Sampling Distribution?

4.10 The Sampling Distribution of (x-bar) and the Central Limit Theorem

5. Inferences Based on a Single Sample: Estimation with Confidence Intervals

5.1 Identifying and Estimating the Target Parameter

5.2 Confidence Interval for a Population Mean: Normal (z) Statistic

5.3 Confidence Interval for a Population Mean: Student's t-statistic

5.4 Large-Sample Confidence Interval for a Population Proportion

5.5 Determining the Sample Size

5.6 Confidence Interval for a Population Variance (Optional)

6. Inferences Based on a Single Sample: Tests of Hypothesis

6.1 The Elements of a Test of Hypothesis

6.2 Formulating Hypotheses and Setting Up the Rejection Region

6.3 Test of Hypothesis About a Population Mean: Normal (z) Statistic

6.4 Observed Significance Levels: p-Values

6.5 Test of Hypothesis About a Population Mean: Student's t-statistic

6.6 Large-Sample Test of Hypothesis About a Population Proportion

6.7 Calculating Type II Error Probabilities: More About β (Optional)

6.8 Test of Hypothesis About a Population Variance (Optional)

6.9 Single Population Inferences

7. Comparing Population Means

7.1 Identifying the Target Parameter

7.2 Comparing Two Population Means: Independent Sampling

7.3 Comparing Two Population Means: Paired Difference Experiments

7.4 Determining the Sample Size

7.5 The Completely Randomized Design: Single Factor

7.6 Comparing Two Populations: Independent Samples

7.7 Comparing Two Populations: Paired Difference Experiment

8. Comparing Population Proportions

8.1 Categorical Data and the Multinomial Distribution

8.2 Testing Categorical Probabilities: One-Way Table

8.3 Testing Categorical Probabilities: Two-Way (Contingency) Table

8.4 A Word of Caution About Chi-Square Tests

8.5 Comparing Two Population Proportions: Independent Sampling

8.6 Determining the Sample Size

9. Simple Linear Regression

9.1 Probabilistic Models

9.2 Fitting the Model: The Least Squares Approach

9.3 Model Assumptions

9.4 Assessing the Utility of the Model: Making Inferences About the Slope β1

9.5 The Coefficients of Correlation and Determination

9.6 Using the Model for Estimation and Prediction

9.7 A Complete Example

9.8 Rank Correlation

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Introduction

STATISTICS IN THE NEW MILLENIUM

This eighth edition of A First Course in Statistics is an introductory text emphasizing inference, with extensive coverage of data collection and analysis as needed to evaluate the reported results of statistical studies and make good decisions. As in earlier editions, the text stresses the development of statistical thinking, the assessment of credibility and value of the inferences made from data, both by those who consume and those who produce them. This one-semester text covers basic statistical and probability topics through simple linear regression. It assumes a mathematical background of basic algebra.

NEW TO THE EIGHTH EDITION

  • Exercise sets have been revised to provide a greater variety in level of difficulty. In addition to "Learning the Mechanics" exercises, "Applied Exercises" are categorized into "Basic," "Intermediate," and "Advanced" at the end of each section.
  • All printouts from statistical software (SAS, SPSS, MINITAB, and STATISTIX) have been revised to reflect the latest versions of the software.
  • The sections in Chapter 8 (Comparing Population Proportions) have been reorganized with emphasis on whether one or two qualitative variables are analyzed.

TRADITIONAL STRENGTHS

We have maintained the pedagogical features of A First Course in Statistics that we believe make it unique among introductory statistics texts. These features, which assist the student in achieving an overview of statistics and an understanding of its relevance in the social and life sciences, business, and everyday life, are as follows:

  • Use of Examples as a TeachingDevice. Almost all new ideas are introduced and illustrated by data-based applications and examples. We believe that students better understand definitions, generalizations, and abstractions after seeing an application.
  • Exploring Data with Statistical Computer Software. Each statistical analysis method presented is demonstrated using output from four leading statistical software packages: SAS, SPSS, MINITAB, or STATISTIX. In addition, output and keystroke instructions for the TI-83 Graphing Calculator are covered in optional boxes.
  • Nonparametric Methods Integrated. Throughout the text, optional sections on alternative distribution-free (nonparametric) procedures follow the relevant sections.
  • Statistics in Action. Each chapter concludes with a case study on a contemporary, controversial, or high-profile issue. Data from the study are presented for analysis and questions, prompting the students to evaluate the findings and to think through the statistical issues involved. Additional cases appear on the Web site.
  • Real Data Exercises. The text includes more than 800 exercises illustrated by applications in almost all areas of research. All the applied exercises employ the use of current real data extracted from a wide variety of publications (e.g., newspapers, magazines, journals, and the Internet). Many students have trouble learning the mechanics of statistical techniques when all problems are couched in terms of realistic applications. For this reason, all exercise sections are divided into four parts:
    • Learning the Mechanics. Designed as straightforward applications of new concepts, these exercises allow students to test their ability to comprehend a mathematical concept or a definition.
    • Applying the Concepts—Basic. Based on applications taken from a wide variety of journals, newspapers, and other sources, these short exercises help the student begin developing the skills necessary to diagnose and analyze real-world problems.
    • Applying the Concepts—Intermediate. Based on more detailed real-world applications, these exercises require students to apply their knowledge of the technique presented in the section.
    • Applying the Concepts—Advanced. These more challenging real-data exercises require students to utilize their critical thinking skills.
  • Quick Review. Each chapter ends with a list of key terms and formulas, with reference to the page number where they first appear.
  • Language Lab. Following the Quick Review is a pronunciation guide to Greek letters and other special terms. Usage notes are also provided.
  • Student Projects. Presented at the end of each chapter, these projects emphasize gathering data, analyzing data, and/or report writing.
  • Footnotes. Although the text is designed for students with a non-calculus background, footnotes explain the role of calculus in various derivations. Footnotes are also used to inform the student about some of the theory underlying certain methods of analysis. These footnotes allow additional flexibility in the mathematical and theoretical level at which the material is presented.
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